"dean86" wrote in message <k9id39$82r$1@newscl01ah.mathworks.com>...> Hi all,> I have the following problem:> 777 input from some different sensors, I have to do a PCA and then a RBF to predict the number of analytes.

What type of sensors?How many measurement vectors for NN input? N = 343?How many dimensions in each input vector? I = 777 ?Regression or classification ?What is the output ?How many dimensions in each output vector? O = ?

> Could you help me to understand the correct way to do the RBF, to use the command 'newrb', but above all, how to understand the results?

> First thing that I do is to load the data, then I have this matrix 777x343, so I do the transpose and I start to do the mean-centring and then the PCA on this matrix and I obtain the scores (343x4) and the loadings (777x4). Now I have to use this scores to do this RBF, so I obtain the transpose of the scores matrix (4x343) and now, should I use the newrb with this last matrix and the original data matrix (777x343)? >What is the criterion for input dimensionality reduction why 777==> 4? This makes no sense to me.

All NEWRB needs are input and output matrices and a reasonable value for the MSE goal and a range of candidate spread values.

For regression standarize both input and target matrices. For c-class classification, standardize the input matrix but use one of c (=O) binary coding for the output matrix.

Use MSEgoal = 0.01*mean(var(t',1)) % yields R^2 >= 0.99

Obtain multiple designs from a loop over spread values. I usually start with a coarse search spread = 2^(i-1), i = 1,2,... Then refine the search if needed.